Developments in the field of deep learning have led to a reexamination of the problem of jet tagging, as new architectures have emerged which can operate on constituent-level data.
Alternative proposals include casting the particle data into a 2D pixel image in order to apply to a CNNs[1]}, [2]}, passing the particle's features one at a time as a sequence into an RNN[3]}, [4]}, [5]}, or embedding groups of nearby constituents into a graph for use in a Graph Neural Networks[6]}.
While RNNs in particular have seen successful application in experiments, more recently the field has been moving to permutation-invariant set based networks[7]}, [8]} which are easier to train and achieve comparable performance.
